Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6523-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6523-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany
UFZ-Helmholtz Centre for Environmental Research, Department Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
Stephan Thober
UFZ-Helmholtz Centre for Environmental Research, Department Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
Luis Samaniego
UFZ-Helmholtz Centre for Environmental Research, Department Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
Bernd Hansjürgens
UFZ-Helmholtz Centre for Environmental Research, Department Economics, Permoserstrasse 15, 04318 Leipzig, Germany
UFZ-Helmholtz Centre for Environmental Research, Department Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
Viewed
Total article views: 3,372 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Jan 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,345 | 932 | 95 | 3,372 | 59 | 60 |
- HTML: 2,345
- PDF: 932
- XML: 95
- Total: 3,372
- BibTeX: 59
- EndNote: 60
Total article views: 2,006 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Dec 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,549 | 380 | 77 | 2,006 | 47 | 52 |
- HTML: 1,549
- PDF: 380
- XML: 77
- Total: 2,006
- BibTeX: 47
- EndNote: 52
Total article views: 1,366 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Jan 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
796 | 552 | 18 | 1,366 | 12 | 8 |
- HTML: 796
- PDF: 552
- XML: 18
- Total: 1,366
- BibTeX: 12
- EndNote: 8
Viewed (geographical distribution)
Total article views: 3,372 (including HTML, PDF, and XML)
Thereof 3,174 with geography defined
and 198 with unknown origin.
Total article views: 2,006 (including HTML, PDF, and XML)
Thereof 1,908 with geography defined
and 98 with unknown origin.
Total article views: 1,366 (including HTML, PDF, and XML)
Thereof 1,266 with geography defined
and 100 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Random forest, an efficient smart technique for analyzing the influence of soil properties on pistachio yield J. Seyedmohammadi et al. 10.1007/s10668-023-03926-2
- Marginal Impact of climate variability on crop yields in Ghana S. Gyamerah et al. 10.1016/j.sciaf.2024.e02314
- Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg F. Brill et al. 10.5194/nhess-24-4237-2024
- Invited perspectives: Challenges and step changes for natural hazard – perspectives from the German Committee for Disaster Reduction (DKKV) B. Thiebes et al. 10.5194/nhess-22-1969-2022
- Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit J. Spišić et al. 10.3390/rs14112596
- On the role of antecedent meteorological conditions on flash drought initialization in Europe J. Shah et al. 10.1088/1748-9326/acd8d3
- Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change L. Li et al. 10.1016/j.eja.2023.126917
- Predicting Potato Diseases in Smallholder Agricultural Areas of Nigeria Using Machine Learning and Remote Sensing-Based Climate Data E. Ibrahim et al. 10.1094/PHYTOFR-10-22-0105-R
- High-resolution drought simulations and comparison to soil moisture observations in Germany F. Boeing et al. 10.5194/hess-26-5137-2022
- Projecting impacts of extreme weather events on crop yields using LASSO regression J. Heilemann et al. 10.1016/j.wace.2024.100738
- Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments J. Gaona et al. 10.1016/j.agwat.2023.108280
- Vegetation Stress Monitor—Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years U. Gessner et al. 10.3390/rs15225428
- Machine learning in crop yield modelling: A powerful tool, but no surrogate for science G. Lischeid et al. 10.1016/j.agrformet.2021.108698
- Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling G. Zhao et al. 10.1029/2022WR032395
- Regionale Dynamik der Pestizid-Konzentration unterhalb der Wurzelzone G. Lischeid et al. 10.1007/s00767-022-00534-1
15 citations as recorded by crossref.
- Random forest, an efficient smart technique for analyzing the influence of soil properties on pistachio yield J. Seyedmohammadi et al. 10.1007/s10668-023-03926-2
- Marginal Impact of climate variability on crop yields in Ghana S. Gyamerah et al. 10.1016/j.sciaf.2024.e02314
- Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg F. Brill et al. 10.5194/nhess-24-4237-2024
- Invited perspectives: Challenges and step changes for natural hazard – perspectives from the German Committee for Disaster Reduction (DKKV) B. Thiebes et al. 10.5194/nhess-22-1969-2022
- Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit J. Spišić et al. 10.3390/rs14112596
- On the role of antecedent meteorological conditions on flash drought initialization in Europe J. Shah et al. 10.1088/1748-9326/acd8d3
- Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change L. Li et al. 10.1016/j.eja.2023.126917
- Predicting Potato Diseases in Smallholder Agricultural Areas of Nigeria Using Machine Learning and Remote Sensing-Based Climate Data E. Ibrahim et al. 10.1094/PHYTOFR-10-22-0105-R
- High-resolution drought simulations and comparison to soil moisture observations in Germany F. Boeing et al. 10.5194/hess-26-5137-2022
- Projecting impacts of extreme weather events on crop yields using LASSO regression J. Heilemann et al. 10.1016/j.wace.2024.100738
- Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments J. Gaona et al. 10.1016/j.agwat.2023.108280
- Vegetation Stress Monitor—Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years U. Gessner et al. 10.3390/rs15225428
- Machine learning in crop yield modelling: A powerful tool, but no surrogate for science G. Lischeid et al. 10.1016/j.agrformet.2021.108698
- Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling G. Zhao et al. 10.1029/2022WR032395
- Regionale Dynamik der Pestizid-Konzentration unterhalb der Wurzelzone G. Lischeid et al. 10.1007/s00767-022-00534-1
Latest update: 25 Dec 2024
Short summary
Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).
Using a statistical model that can also take complex systems into account, the most important...